IGME: Efficient Chained Method Ensemble for Transferable Semantic Segmentation Attacks
Quick Answer
The IGME method offers an efficient single-source approach for transferable semantic segmentation attacks, significantly reducing computational costs compared to ensemble attacks.
Quick Take
It utilizes a chained computation of differentiable components and an integrated-gradient-style stabilization heuristic, achieving competitive transferability on Pascal VOC and Cityscapes datasets with only one source model.
Key Points
- IGME reduces computational costs by using a single-source model for attacks.
- Chained computation allows sharing of expensive source-model gradient calculations.
- Integrated-gradient-style stabilization improves update stability during attacks.
- Achieves competitive transferability compared to single-source baselines.
- Evaluated on Pascal VOC and Cityscapes with CNN and transformer models.
Paper Resources
Source Excerpt
Semantic segmentation models are vulnerable to transferable adversarial perturbations, yet evaluating transfer attacks on dense prediction models can be computationally expensive. Existing ensemble attacks often rely on multiple surrogate models, increasing the computation cost, even harder for segmentation. This paper studies an efficient single-source alternative for transferable attacks on semantic segmentation. We formulate transferable attack composition as a chained computation over differ
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